SFA-based ELM for remote detection of stationary objects

Liang, Xiaolin; Zhu, Weifeng; Sun, Jiawen; Deng, Jianqin

doi:10.1007/s12652-021-03131-x

Cited by 1 publication

(4 citation statements)

References 42 publications

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“…This topic is of particular relevance and complexity in indoor situations were traditional GPS system cannot aid in the localisation. [193]- [196] Human orientation (n = 2)…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

“…Four papers [193]- [196] deal with human localisation taking RADAR-based extracted vital sign as input. Overall, we can notice a tendency to increase the subject distance to 20 m, instead of working at the short range as in other applications.…”

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

“…Overall, we can notice a tendency to increase the subject distance to 20 m, instead of working at the short range as in other applications. [193] employs MAE reporting an error of localisation ranging between 15 to 31 cm in outdoor conditions and 11 to 27 cm in indoor conditions; [194] employs RMSE with errors ranging from 12.3 cm to 20.7 cm; [196] uses as a metric the percentage of correct localisation within 30 cm or 10 cm error reporting a percentage from 2% to 15% with localisation based on breathing features only localisation and under 10% with localisation based on heartbeat features. All the included papers uses a UWB RADAR technology with two reported operating frequencies of 400 MHz and 4.3 GHz.…”

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

“…All the included papers uses a UWB RADAR technology with two reported operating frequencies of 400 MHz and 4.3 GHz. The used models are KNN [195] or variants [194], SVM [196], ELM [193] and a CNN [194]. Only in the case of Liu et al [194] subjects involved are localised while moving.…”

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

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Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

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In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

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“…This topic is of particular relevance and complexity in indoor situations were traditional GPS system cannot aid in the localisation. [193]- [196] Human orientation (n = 2)…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

Section: B Monitoring Of Human Behaviourmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

SFA-based ELM for remote detection of stationary objects

Cited by 1 publication

References 42 publications

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

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